Ashutosh Shandilya

1.0k total citations
25 papers, 845 citations indexed

About

Ashutosh Shandilya is a scholar working on Molecular Biology, Computational Theory and Mathematics and Organic Chemistry. According to data from OpenAlex, Ashutosh Shandilya has authored 25 papers receiving a total of 845 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 8 papers in Computational Theory and Mathematics and 7 papers in Organic Chemistry. Recurrent topics in Ashutosh Shandilya's work include Computational Drug Discovery Methods (8 papers), Chemical Synthesis and Analysis (5 papers) and Cholinesterase and Neurodegenerative Diseases (4 papers). Ashutosh Shandilya is often cited by papers focused on Computational Drug Discovery Methods (8 papers), Chemical Synthesis and Analysis (5 papers) and Cholinesterase and Neurodegenerative Diseases (4 papers). Ashutosh Shandilya collaborates with scholars based in India, Japan and Bulgaria. Ashutosh Shandilya's co-authors include Durai Sundar, Abhinav Grover, Virendra S. Bisaria, B. Jayaram, Nasimul Hoda, Ehtesham Jameel, V. Haridas, Jitendra Kumar, Md. Imtaiyaz Hassan and Faizan Ahmad and has published in prestigious journals such as PLoS ONE, Chemical Communications and Scientific Reports.

In The Last Decade

Ashutosh Shandilya

25 papers receiving 825 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ashutosh Shandilya India 15 336 237 204 172 120 25 845
Prem Prakash Kushwaha India 19 453 1.3× 94 0.4× 108 0.5× 200 1.2× 54 0.5× 43 983
Olujide O. Olubiyi Nigeria 18 383 1.1× 54 0.2× 173 0.8× 169 1.0× 31 0.3× 40 796
Jubie Selvaraj India 16 357 1.1× 47 0.2× 421 2.1× 140 0.8× 76 0.6× 104 1.0k
Sean A. Hudson United Kingdom 9 661 2.0× 37 0.2× 179 0.9× 205 1.2× 163 1.4× 10 1.2k
Sang Hoon Joo South Korea 18 811 2.4× 54 0.2× 207 1.0× 49 0.3× 90 0.8× 55 1.2k
Hamadeh Tarazi United Arab Emirates 17 338 1.0× 37 0.2× 287 1.4× 97 0.6× 131 1.1× 37 769
Shama Khan South Africa 17 389 1.2× 36 0.2× 98 0.5× 176 1.0× 83 0.7× 44 705
Shweta Jain India 19 585 1.7× 28 0.1× 501 2.5× 89 0.5× 71 0.6× 49 1.4k
Bruna Bizzarri Italy 23 386 1.1× 45 0.2× 1.1k 5.2× 83 0.5× 324 2.7× 35 1.6k
Gousia Chashoo India 19 383 1.1× 42 0.2× 407 2.0× 43 0.3× 134 1.1× 39 899

Countries citing papers authored by Ashutosh Shandilya

Since Specialization
Citations

This map shows the geographic impact of Ashutosh Shandilya's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ashutosh Shandilya with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ashutosh Shandilya more than expected).

Fields of papers citing papers by Ashutosh Shandilya

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ashutosh Shandilya. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ashutosh Shandilya. The network helps show where Ashutosh Shandilya may publish in the future.

Co-authorship network of co-authors of Ashutosh Shandilya

This figure shows the co-authorship network connecting the top 25 collaborators of Ashutosh Shandilya. A scholar is included among the top collaborators of Ashutosh Shandilya based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ashutosh Shandilya. Ashutosh Shandilya is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Kumar, Jitendra, Anju Singh, Ashutosh Shandilya, et al.. (2018). Pyrimidine‐Triazolopyrimidine and Pyrimidine‐Pyridine Hybrids as Potential Acetylcholinesterase Inhibitors for Alzheimer's Disease. ChemistrySelect. 3(2). 736–747. 32 indexed citations
2.
Shandilya, Ashutosh, et al.. (2016). Chiral and non-chiral assemblies from lipidated serine-based pseudopeptidic molecules. Molecular Systems Design & Engineering. 1(2). 163–168. 7 indexed citations
3.
Kumar, Jitendra, Poonam Meena, Anju Singh, et al.. (2016). Synthesis and screening of triazolopyrimidine scaffold as multi-functional agents for Alzheimer's disease therapies. European Journal of Medicinal Chemistry. 119. 260–277. 63 indexed citations
4.
Maqbool, Mudasir, Apra Manral, Ehtesham Jameel, et al.. (2016). Development of cyanopyridine–triazine hybrids as lead multitarget anti-Alzheimer agents. Bioorganic & Medicinal Chemistry. 24(12). 2777–2788. 54 indexed citations
5.
Shandilya, Ashutosh, Nasimul Hoda, Sameena Khan, et al.. (2016). De novo lead optimization of triazine derivatives identifies potent antimalarials. Journal of Molecular Graphics and Modelling. 71. 96–103. 6 indexed citations
6.
7.
Hoda, Nasimul, Huma Naz, Ehtesham Jameel, et al.. (2015). Curcumin specifically binds to the human calcium–calmodulin-dependent protein kinase IV: fluorescence and molecular dynamics simulation studies. Journal of Biomolecular Structure and Dynamics. 34(3). 572–584. 71 indexed citations
8.
Naz, Huma, Ehtesham Jameel, Nasimul Hoda, et al.. (2015). Structure guided design of potential inhibitors of human calcium–calmodulin dependent protein kinase IV containing pyrimidine scaffold. Bioorganic & Medicinal Chemistry Letters. 26(3). 782–788. 35 indexed citations
9.
Shandilya, Ashutosh, et al.. (2015). Peptide dendrimers with designer core for directed self-assembly. Tetrahedron. 71(46). 8758–8765. 14 indexed citations
10.
Shandilya, Ashutosh, Sajeev Chacko, B. Jayaram, & Indira Ghosh. (2013). A plausible mechanism for the antimalarial activity of artemisinin: A computational approach. Scientific Reports. 3(1). 2513–2513. 60 indexed citations
11.
Haridas, V., et al.. (2013). Bispidine as a helix inducing scaffold: examples of helically folded linear peptides. Chemical Communications. 49(93). 10980–10980. 11 indexed citations
12.
Grover, Abhinav, Rumani Singh, Ashutosh Shandilya, et al.. (2012). Ashwagandha Derived Withanone Targets TPX2-Aurora A Complex: Computational and Experimental Evidence to its Anticancer Activity. PLoS ONE. 7(1). e30890–e30890. 42 indexed citations
13.
Grover, Abhinav, et al.. (2012). Computational Evidence to Inhibition of Human Acetyl Cholinesterase by Withanolide A for Alzheimer Treatment. Journal of Biomolecular Structure and Dynamics. 29(4). 651–662. 43 indexed citations
14.
15.
Grover, Abhinav, Didik Priyandoko, Ran Gao, et al.. (2011). Withanone binds to mortalin and abrogates mortalin–p53 complex: Computational and experimental evidence. The International Journal of Biochemistry & Cell Biology. 44(3). 496–504. 57 indexed citations
16.
Grover, Abhinav, et al.. (2011). Hsp90/Cdc37 Chaperone/co-chaperone complex, a novel junction anticancer target elucidated by the mode of action of herbal drug Withaferin A. BMC Bioinformatics. 12(S1). S30–S30. 73 indexed citations
17.
Haridas, V., et al.. (2011). Bispidine as a secondary structure nucleator in peptides. Tetrahedron Letters. 53(6). 623–626. 28 indexed citations
18.
Grover, Abhinav, Ashutosh Shandilya, Virendra S. Bisaria, & Durai Sundar. (2010). Probing the anticancer mechanism of prospective herbal drug Withaferin A on mammals: a case study on human and bovine proteasomes. BMC Genomics. 11(S4). S15–S15. 46 indexed citations
19.
20.
Grover, Abhinav, et al.. (2010). Blocking the chaperone kinome pathway: Mechanistic insights into a novel dual inhibition approach for supra-additive suppression of malignant tumors. Biochemical and Biophysical Research Communications. 404(1). 498–503. 14 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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